The present application pertains to the diagnostic imaging arts. It finds particular application in conjunction with single photon emission computed tomography (SPECT) and will be described with particular reference thereto. It is to be appreciated, however, that it may also be applicable to other types of nuclear imaging, as well as other types of diagnostic imaging.
In SPECT imaging, a patient is injected with a radioactive tracer which breaks down to emit gamma rays of a characteristic energy. One or more SPECT detector heads are positioned adjacent the patient and rotated to a plurality of angular orientations. The data collected at each angular orientation represents projection data depicting the distribution of the radioactive tracer in the patient. These projections are then reconstructed into a three-dimensional diagnostic image.
A SPECT acquisition typically takes about 10 minutes or more to complete. During that time, the patient may move. Further, internal organs may also move during the imaging time. This motion, of course, causes motion-artifacts.
A commonly-used technique for motion-correction is described in U.S. Pat. No. 5,552,605. The projection data is first reconstructed into an image-artifacted three-dimensional image. The three-dimensional image is then forward-projected along each of the projection directions to generate a series of re-projections. The region of interest in each original projection and re-projection corresponding to the same angular orientation are compared and the original projections are shifted into alignment with the corresponding re-projection. The shifted original projections are reconstructed into a motion-correction 3D volume image. This process may be iteratively repeated to refine the motion-correction.
Although the technique of U.S. Pat. No. 5,552,605 has been used commercially for many years to generate motion-corrected SPECT images, there is room for improvement. Particularly, due to other degrading factors such as attenuation, scatter, system blurring, the shift amount even with the best match of the re-projection and the actually measured projection may not reflect the actual motion amount. In some cases, a significant artificial motion may be introduced which may compromise motion-correction success and accuracy.
The present application improves the motion-correction and accuracy by recognizing image degrading factors such as attenuation, scatter, and system resolution and considering them during iterative image reconstruction and motion-correction.